






"""simple docstring"""





import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		trt.Logger(trt.Logger.WARNING)
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE   :							Dict          =		argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=384,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=128,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

_SCREAMING_SNAKE_CASE   :							Tuple          =		parser.parse_args()

if args.tokenizer_name:
  _SCREAMING_SNAKE_CASE   :							int          =		AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
  raise ValueError(
      '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
      '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
  )

logger.info('''Training/evaluation parameters %s''', args)

_SCREAMING_SNAKE_CASE   :							List[str]          =		args.per_device_eval_batch_size

_SCREAMING_SNAKE_CASE   :							Dict          =		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_SCREAMING_SNAKE_CASE   :							Dict          =		True

_SCREAMING_SNAKE_CASE   :							List[Any]          =		'''temp_engine/bert-fp32.engine'''
if args.fpaa:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		'''temp_engine/bert-fp16.engine'''
if args.inta:
  _SCREAMING_SNAKE_CASE   :							Dict          =		'''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
  os.makedirs('''temp_engine''')

_SCREAMING_SNAKE_CASE   :							str          =		1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
  with open(args.onnx_model_path, '''rb''') as model:
    if not parser.parse(model.read()):
      for error in range(parser.num_errors):
        print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
  _SCREAMING_SNAKE_CASE   :							Optional[int]          =		[network.get_input(i) for i in range(network.num_inputs)]
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

  with builder.create_builder_config() as config:
    _SCREAMING_SNAKE_CASE   :							int          =		1 << 50
    if STRICT_TYPES:
      config.set_flag(trt.BuilderFlag.STRICT_TYPES)
    if args.fpaa:
      config.set_flag(trt.BuilderFlag.FPaa)
    if args.inta:
      config.set_flag(trt.BuilderFlag.INTa)
    _SCREAMING_SNAKE_CASE   :							Any          =		builder.create_optimization_profile()
    config.add_optimization_profile(profile)
    for i in range(len(input_names)):
      profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
    _SCREAMING_SNAKE_CASE   :							Optional[int]          =		builder.build_engine(network, config)

    # serialize_engine and store in file (can be directly loaded and deserialized):
    with open(engine_name, '''wb''') as f:
      f.write(engine.serialize())







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						Optional[int]					) ->							Optional[Any]:
  lowerCamelCase_       	=		np.asarray(inputs['input_ids']						,							dtype=np.intaa					)
  lowerCamelCase_       	=		np.asarray(inputs['attention_mask']						,							dtype=np.intaa					)
  lowerCamelCase_       	=		np.asarray(inputs['token_type_ids']						,							dtype=np.intaa					)

  # Copy inputs
  cuda.memcpy_htod_async(d_inputs[0]						,							input_ids.ravel()						,							_lowerCamelCase					)
  cuda.memcpy_htod_async(d_inputs[1]						,							attention_mask.ravel()						,							_lowerCamelCase					)
  cuda.memcpy_htod_async(d_inputs[2]						,							token_type_ids.ravel()						,							_lowerCamelCase					)
  # start time
  lowerCamelCase_       	=		time.time()
  # Run inference
  context.execute_async(
      bindings=[int(_lowerCamelCase					) for d_inp in d_inputs] + [int(_lowerCamelCase					), int(_lowerCamelCase					)]						,							stream_handle=stream.handle					)
  # Transfer predictions back from GPU
  cuda.memcpy_dtoh_async(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)
  cuda.memcpy_dtoh_async(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)
  # Synchronize the stream and take time
  stream.synchronize()
  # end time
  lowerCamelCase_       	=		time.time()
  lowerCamelCase_       	=		end_time - start_time
  lowerCamelCase_       	=		(h_outputa, h_outputa)
  # print(outputs)
  return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
  datasets.utils.logging.set_verbosity_warning()
  transformers.utils.logging.set_verbosity_info()
else:
  datasets.utils.logging.set_verbosity_error()
  transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
  set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  _SCREAMING_SNAKE_CASE   :							str          =		load_dataset(args.dataset_name, args.dataset_config_name)
else:
  raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

_SCREAMING_SNAKE_CASE   :							List[str]          =		raw_datasets['''validation'''].column_names

_SCREAMING_SNAKE_CASE   :							Tuple          =		'''question''' if '''question''' in column_names else column_names[0]
_SCREAMING_SNAKE_CASE   :							Tuple          =		'''context''' if '''context''' in column_names else column_names[1]
_SCREAMING_SNAKE_CASE   :							List[Any]          =		'''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
  logger.warning(
      F'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
      F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
  )

_SCREAMING_SNAKE_CASE   :							List[str]          =		min(args.max_seq_length, tokenizer.model_max_length)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]					) ->							Optional[int]:
  # Some of the questions have lots of whitespace on the left, which is not useful and will make the
  # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
  # left whitespace
  lowerCamelCase_       	=		[q.lstrip() for q in examples[question_column_name]]

  # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
  # in one example possible giving several features when a context is long, each of those features having a
  # context that overlaps a bit the context of the previous feature.
  lowerCamelCase_       	=		tokenizer(
      examples[question_column_name if pad_on_right else context_column_name]						,							examples[context_column_name if pad_on_right else question_column_name]						,							truncation='only_second' if pad_on_right else 'only_first'						,							max_length=_lowerCamelCase						,							stride=args.doc_stride						,							return_overflowing_tokens=_lowerCamelCase						,							return_offsets_mapping=_lowerCamelCase						,							padding='max_length'						,							)

  # Since one example might give us several features if it has a long context, we need a map from a feature to
  # its corresponding example. This key gives us just that.
  lowerCamelCase_       	=		tokenized_examples.pop('overflow_to_sample_mapping'					)

  # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
  # corresponding example_id and we will store the offset mappings.
  lowerCamelCase_       	=		[]

  for i in range(len(tokenized_examples['input_ids']					)					):
    # Grab the sequence corresponding to that example (to know what is the context and what is the question).
    lowerCamelCase_       	=		tokenized_examples.sequence_ids(_lowerCamelCase					)
    lowerCamelCase_       	=		1 if pad_on_right else 0

    # One example can give several spans, this is the index of the example containing this span of text.
    lowerCamelCase_       	=		sample_mapping[i]
    tokenized_examples["example_id"].append(examples['id'][sample_index]					)

    # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
    # position is part of the context or not.
    lowerCamelCase_       	=		[
        (o if sequence_ids[k] == context_index else None)
        for k, o in enumerate(tokenized_examples['offset_mapping'][i]					)
    ]

  return tokenized_examples


_SCREAMING_SNAKE_CASE   :							int          =		raw_datasets['''validation''']
# Validation Feature Creation
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

_SCREAMING_SNAKE_CASE   :							Any          =		default_data_collator

_SCREAMING_SNAKE_CASE   :							Dict          =		eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_SCREAMING_SNAKE_CASE   :							Dict          =		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						Union[str, Any]="eval"					) ->							Optional[int]:
  # Post-processing: we match the start logits and end logits to answers in the original context.
  lowerCamelCase_       	=		postprocess_qa_predictions(
      examples=_lowerCamelCase						,							features=_lowerCamelCase						,							predictions=_lowerCamelCase						,							version_2_with_negative=args.version_2_with_negative						,							n_best_size=args.n_best_size						,							max_answer_length=args.max_answer_length						,							null_score_diff_threshold=args.null_score_diff_threshold						,							output_dir=args.output_dir						,							prefix=_lowerCamelCase						,							)
  # Format the result to the format the metric expects.
  if args.version_2_with_negative:
    lowerCamelCase_       	=		[
        {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
    ]
  else:
    lowerCamelCase_       	=		[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

  lowerCamelCase_       	=		[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=_lowerCamelCase						,							label_ids=_lowerCamelCase					)


_SCREAMING_SNAKE_CASE   :							Any          =		load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
  # setup for TRT inferrence
  for i in range(len(input_names)):
    context.set_binding_shape(i, INPUT_SHAPE)
  assert context.all_binding_shapes_specified







  def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[int]					) ->							Any:
    return trt.volume(engine.get_binding_shape(_lowerCamelCase					)					) * engine.get_binding_dtype(_lowerCamelCase					).itemsize

  # Allocate device memory for inputs and outputs.
  _SCREAMING_SNAKE_CASE   :							Tuple          =		[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

  # Allocate output buffer
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
  _SCREAMING_SNAKE_CASE   :							Tuple          =		cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		cuda.mem_alloc(h_outputa.nbytes)
  _SCREAMING_SNAKE_CASE   :							Dict          =		cuda.mem_alloc(h_outputa.nbytes)

  # Create a stream in which to copy inputs/outputs and run inference.
  _SCREAMING_SNAKE_CASE   :							Any          =		cuda.Stream()

  # Evaluation
  logger.info('''***** Running Evaluation *****''')
  logger.info(F'''  Num examples = {len(eval_dataset)}''')
  logger.info(F'''  Batch size = {args.per_device_eval_batch_size}''')

  _SCREAMING_SNAKE_CASE   :							Dict          =		0.0
  _SCREAMING_SNAKE_CASE   :							List[str]          =		0
  _SCREAMING_SNAKE_CASE   :							Any          =		timeit.default_timer()

  _SCREAMING_SNAKE_CASE   :							Dict          =		None
  for step, batch in enumerate(eval_dataloader):
    _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
    total_time += infer_time
    niter += 1

    _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							List[Any]          =		outputs
    _SCREAMING_SNAKE_CASE   :							List[str]          =		torch.tensor(start_logits)
    _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		torch.tensor(end_logits)

    # necessary to pad predictions and labels for being gathered
    _SCREAMING_SNAKE_CASE   :							Any          =		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
    _SCREAMING_SNAKE_CASE   :							List[str]          =		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

    _SCREAMING_SNAKE_CASE   :							List[Any]          =		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
    _SCREAMING_SNAKE_CASE   :							Dict          =		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

  if all_preds is not None:
    _SCREAMING_SNAKE_CASE   :							Optional[int]          =		nested_truncate(all_preds, len(eval_dataset))

  _SCREAMING_SNAKE_CASE   :							Tuple          =		timeit.default_timer() - start_time
  logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
  # Inference time from TRT
  logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
  logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))






  logger.info('''Total Number of Inference =  %d''', niter)

_SCREAMING_SNAKE_CASE   :							Dict          =		post_processing_function(eval_examples, eval_dataset, all_preds)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)






logger.info(F'''Evaluation metrics: {eval_metric}''')












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[str]          =		{}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							str          =		['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Any          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




